A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol’s known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.


Supplementary Figure S2. Effects of excitatory-inhibitory ratio on membrane potential and spectral slope depend on leak conductance.
a Left: histogram of E:I ratios across 20,000 simulations with parameters sampled from the distributions in Fig. 6a.Simulations were binned into five categories, from low to high   :   ratios.Middle: histogram of somatic membrane potential for simulations with a low leak conductance (  < 1 mS cm -2 ), divided into the five E:I ratio categories.The average membrane potential is not largely affected by the E:I ratio in this high leak conductance condition.Right: histogram of somatic membrane potential for simulations with a high leak conductance (  > 1 mS cm -2 ).A high E:I ratio significantly shifts the distribution of membrane potential to more hyperpolarized values.Blue and red vertical lines show the reversal potential of GABARs and AMARs, respectively.

b1-5
Power spectra of the rate functions for each type of synaptic input depicted in a1-5, respectively.

c1-5
Unitary spectra associated with input depicted in a1-5, before and after changes to a biophysical parameter, including   that was increased from 10 ms (black) to 30 ms (red) in the first column,   that was increased from -60 mV (black) to -45 mV (red) in the second column, and   and   that were increased from 0.7 nS (black) to 1.4 nS (red) in the third and fourth columns, respectively.

Supplementary Figure S4. Detrending with Lorentzian function corrects for changes in biophysical parameters.
a Unitary spectra from Fig. S3, fit with the sum of two Lorentzian functions (Eq.1; solid gray lines).The leftmost column shows the unitary spectrum with default parameters.The Lorentzian fit for the default parameters are also displayed in the plots in the other columns as a dashed grey line.
b Unitary spectra from a, detrended by dividing by the fitted Lorentzian function (solid gray lines).Colours correspond to the various parameter changes from a.Note that, for each type of input dynamics, the detrended spectra have similar profiles, i.e., the effects of the parameter changes have been corrected.
Supplementary Figure S5.Spectral changes with respect to LOC-aligned time.
a Top: example fits to the average EEG spectrum of each patient at baseline using Eq.6, while fixing the parameters   = 4 ms and  1 = 20 ms.Note the difference between the fitted Eq. 6 and the low frequency power (< 3 Hz), which our model predicts is caused by neural dynamics and not synaptic timescales.This low frequency power was fit here with a Gaussian peak function as per the FOOOF methods 2 (Gaussian fits not shown).Bottom: detrended power in decibels.
b Same as a, but for fits to spectra -10 to 0 s prior to LOC.Here,  1 was not fixed and its estimated value for each patient is printed in blue.
c Example EEG from patient 13, split into five, 2 s windows (top).The power spectrum of each window is shown below, along with the fitted synaptic timescales (Eq.6) in red.
d Same as Fig. 9d & h, but band power is plotted here against time relative to LOC.Data plotted as mean and shading represents 95% confidence interval of mean.
Supplementary Figure S6.Changes in estimated   exhibit similar dynamics across recording locations.
The plot labelled Cz is identical to Fig. 8e.The other plots show the dynamics of  1 for the other EEG channels.
For each plot, the corresponding recording site is printed above.Supplementary Table S1 7 .b Morphologies without NeuorMoprho (NMO) or ModelDB (MDB) IDs were accessed from the code repository associated with Hagen et al. 7 .All morphology files are also supplied in the code repository associated with this paper 8 .

. Representative neuron morphologies used in the model a .
Morphologies and relative abundances were identical to Hagen et al. a